A holistic statistical modelling approach to quantitative discovery proteomics and metabolomics for underpinning integrative systems medicine

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Medical researchers are increasing wishing to understand the complex interactions between the building blocks of genes, metabolites and proteins that control human function, how they break down under disease and how this breakdown can be averted. The field of systems biology has emerged to overcome the deficiencies of the traditional reductionist approach, which has identified the building blocks themselves and many of the individual interactions but has not been able to deduce how systems of these blocks act and react in unison. The application of systems biology is widespread, as it promises to revolutionise our understanding of healthy processes in plants, animals and humans. This huge body of evidence from life sciences research provides ample justification for the widespread potential in translation to systems medicine, for empowering medical research, biomarker discovery and personalised medicine. Often the systems medicine approach starts with snapshots of a particular biological sample and supporting readings or clinical data. Mass spectrometry is a pervasive technique for gaining a snapshot of a sample, and it does this by ionising the sample and then measuring each constituent compound's mass and quantity based on the resulting charge. This is often not enough to separate out the sample fully and therefore a preceding phase of liquid or gas chromatography is used to provide an initial separation. Due to technical and biological variations, it is necessary to analyse multiple samples to get reliable readings. Furthermore, classes of protein and metabolites require different sample preparation, different chromatography settings and different types of mass spectrometry instrumentation. These all add different kinds of biases and variation. Moreover, in biomedical research, despite stringent control of confounding factors in experimental design, a step-change in complexity and variation is evident within typical disease models and clinical samples. Unfortunately, bioanalytical and bioinformatics methodology for protein and metabolite mass spectrometry is fundamentally reliant on the simplifying characteristics of well-controlled systems biology studies, and performs poorly on complex biomedical samples. Since the datasets are so large, the existing computational techniques tend to convert the rich raw data from mass spectrometry output to a symbolic representation of compounds too early on. The integration of the complement of protein and metabolite measurements from biomedical samples into rigorous statistical models for translational research, clinical trial design and clinical diagnostic and prognostic prediction is reliant on their appropriate and accurate statistical handling. Unfortunately, this is exceptionally problematic with current approaches.We instead advocate all experimental raw data across proteins, metabolites and gene expression should be modelled together, so statistical 'strength' can be borrowed across the collection when making decisions about whether a compound or compound interaction truly exists in the data and at what level of confidence and relative quantity between health and disease. We propose that with a holistic model precisely evaluating all the statistical variation and bias across complete experimental designs, we can significantly increase our understanding of underlying variations in mass spectrometry experiments in the clinical setting and provide an enabling pathway to improving data analysis and interpretation, ultimately leading to enhanced sensitivity and robustness of these technologies to benefit translational and clinical research.